23 assistant-professor-computer-science-data PhD positions at ETH Zurich in Switzerland
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100%, Zurich, fixed-term The Frazzoli group at the Institute for Dynamic Systems and Control (IDSC), Department of Mechanical and Process Engineering (D-MAVT) is looking for a Ph.D. student in the
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nanofabrication, optics, optomechanics, vacuum technology, programming for experiment control and data evaluation is highly valued but not mandatory. Workplace Workplace We offer We are an international and
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balloon system and assisting with UAV/floating balloon operations. Analyzing in situ data (holographic imagers) and ground-based remote-sensing data (cloud radar) to derive ice crystal growth rates via
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as possible. Applications are evaluated on a rolling basis until the position is filled. Profile Master's degree in Materials Science, Chemistry, Mechanical Engineering, Electrical Engineering, or
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, Geophysics, Computer Science, or a related field. A solid grasp of general physics and a keen interest in the physics of wave propagation. Strong programming skills in Python, Matlab, or similar languages. You
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: Undergraduate or (preferably) Master’s degree in a social science program with a preference for Political Science, Economics, Sociology, Data Science, Computational Social Science, or in a closely related
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. For more information, visit msrl.ethz.ch. Job description This position is part of the GREENS project, a Marie Skłodowska-Curie Doctoral Network offering the opportunity to pursue a PhD across various
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100%, Zurich, fixed-term Exercise Physiology Lab, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology Project background Respiration and its regulation
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exploration. For more information, visit visit msrl.ethz.ch. Job description This position is part of the GREENS project, a Marie Skłodowska-Curie Doctoral Network offering the opportunity to pursue a PhD
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100%, Zurich, fixed-term The research group of Prof. Simonov at ETH Zurich is seeking a motivated PhD candidate interested in developing computational approaches for understanding disorder in